Staračka domaćinstva u Srbiji od početka 21. veka - (socio)demografska perspektiva

Objective: Longevity fails to account for health and functional status during aging. We sought to quantify differences in years of total life, years of healthy life, and years of able life among groups defined by age, sex, and race. Design: Primary analysis of a cohort study. Setting: 18 years of annual evaluations in four U.S. communities. Participants: 5888 men and women aged 65 and older. Measurements: Years of life were calculated as the time from enrollment to death or 18 years. Years of total, healthy, and able life were determined from self-report during annual or semi-annual contacts. Cumulative years were summed across each of the age and sex groups. Results: White women had the best outcomes for all three measures, followed by white men, non-white women, and non-white men. For example, at the mean age of 73, a white female participant could expect 12.9 years of life, 8.9 of healthy life and 9.5 of able life, while a non-white female could expect 12.6, 7.0, and 8.0 years, respectively. A white male could expect 11.2, 8.1, and 8.9 years of life, healthy life, and able life, and a non-white male 10.3, 6.2, and 7.9 years. Regardless of starting age, individuals of the same race and sex groups spent similar amounts (not proportions) of time in an unhealthy or unable state. Conclusion: Gender had a greater effect on longevity than did race, but race had a greater effect on years spent healthy or able. The mean number of years spent in an unable or sick state was surprisingly independent of the lifespan.

Objective: To create personalized estimates of future health and ability status for older adults. Method: Data came from the Cardiovascular Health Study (CHS), a large longitudinal study. Outcomes included years of life, years of healthy life (based on self-rated health), years of able life (based on activities of daily living), and years of healthy and able life. We developed regression estimates using the demographic and health characteristics that best predicted the four outcomes. Internal and external validity were assessed. Results: A prediction equation based on 11 variables accounted for about 40% of the variability for each outcome. Internal validity was excellent, and external validity was satisfactory. The resulting CHS Healthy Life Calculator (CHSHLC) is available at http://healthylifecalculator.org. Conclusion: CHSHLC provides a well-documented estimate of future years of healthy and able life for older adults, who may use it in planning for the future.

Little is known about how many years of life and disability-free years seniors can gain through exercise. Using data from the Cardiovascular Health Study, the authors estimated the extra years of life and self-reported healthy life (over 11 years) and years without impairment in activities of daily living (over 6 years) associated with quintiles of physical activity (PA) in older adults from different age groups. They estimated PA from the Minnesota Leisure Time Activities Questionnaire. Multivariable linear regression adjusted for health-related covariates. The relative gains in survival and years of healthy life (YHL) generally were proportionate to the amount of PA, greater among those 75+, and higher in men. Compared with being sedentary, the most active men 75+ had 1.49 more YHL (95% CI: 0.79, 2.19), and the most active women 75+ had 1.06 more YHL (95% CI: 0.44, 1.68). Seniors over age 74 experience the largest relative gains in survival and healthy life from physical activity.

Our objective was to assess morbidity trends in Europe and to classify European countries based on population ageing theories: the compression, expansion and dynamic equilibrium of morbidity. The proportions of healthy life years were calculated for 31 European countries for the period 2005-2019 based on life expectancy values and healthy life years at age 65years adopted from the Eurostat database. European countries were classified according to morbidity patterns applying the standard deviation distance from the average of relative change method between the selected years. A large degree of variation in terms of life expectancy and healthy life years at age 65years was determined between 2005 and 2019. While the life expectancy differences between men and women were consistent across all the European countries, the gender gap concerning healthy life years was more diverse. Approximately one-third of the countries fell into the expansion, compression and dynamic equilibrium categories, respectively. Significant variations were identified in healthy life year trends across European countries, which underscores the need for preventive strategies.

BackgroundThe objective of this paper is to provide analytical research that supported the European Commission in setting the global target of additional two healthy life years (HLY) at birth by 2020 in the EU on average, within the European Innovation Partnership on Active and Healthy Ageing (the EIP on AHA). It produces a straightforward analysis of HLY projections that helped the European Commission set a firm, politically sound, target. In order to reach that goal, policy makers need to commit to redefining health priorities and goals and developing and implementing relevant strategies and programmes.MethodsThe study computes a simple simulation of the HLY at birth based on three demographic scenarios: compression of morbidity, expansion of morbidity and an intermediary scenario, the dynamic equilibrium, given the expected 2.1 year gain in male and 1.6 in female life expectancy (LE) by 2020. Data on HLY and projections of life expectancy were obtained from Eurostat and 2008 was taken as a baseline. For consistency and given data gaps, EU27 average values of HLY were calculated.ResultsIn the EU27 as a whole, the difference between LE and HLY in 2008 was nearly 15 years for men and 20 years for women. The developments of healthy life expectancies across the EU Member States (MSs) are even more diverse that makes it difficult to model any robust EU level trends.Under compression of morbidity, life expectancy and HLY would increase by 2020 on average by 2.1 and 2.0 years for men and by 1.6 and 1.4 years for women respectively. The expected years with disability would remain unchanged while the HLY/LE ratio would improve leading to a 0.5% gain for both genders. Under expansion of morbidity, life expectancy would increase by 2.1 years for men and 1.4 years for women by 2020, while HLY would remain unchanged and the expected years with disability would increase by 2.1 years and 1.6 years in women. This would imply the deterioration of the HLY/LE ratio for both men and women generating a 2.2% and 1.4% loss of health for men and women accordingly. Under dynamic equilibrium, the HLY would increase but to a lesser extent as the rise in life expectancy. The HLY would increase by 1.6 and 1.2 years for men and women respectively. HLY/LE ratio would remain unchanged for both men (+0.1%) and women. The study shows that the first scenario would reduce the HLY gap between the EU MSs by 1.4 years in men and 1.2 years in women, the second would generate no change, while the third one would reduce the gap by 0.9 years in men and increase it by 0.7 years in women.ConclusionsThe results of the study triggered the political decision of setting the global target of 2 additional HLY for the European Innovation Partnership on Active and Healthy Ageing to be achieved by 2020. It is a ‘grand’ goal but can be achieved. Statistics clearly show that EU countries characterise very different levels of health progress, with a gap of 2 decades and diverging trends. With this in mind, the EU HLY target should be complemented by national HLY targets for men and women, set by MSs.

Putting users at the heart of care: engaging the “cared-for” in integrated innovation

To determine whether elderly people with different patterns of magnetic resonance imaging (MRI) findings have different long-term outcomes. Longitudinal cohort study. Cardiovascular Health Study. Individuals aged 65 and older were recruited (N = 5,888); 3,660 of these underwent MRI, and 3,230 without a stroke before MRI were included in these analyses. Cluster analysis of brain MRI findings was previously used to define five clusters: normal, atrophy, simple infarct, leukoaraiosis, and complex infarct. Participants were subsequently classified as healthy if they rated their health as excellent, very good, or good and as able if they did not report any limitations in activities of daily living (ADLs). Mean years of life (YoL), years of healthy life (YHL), and years of able life (YAL) were calculated over 16 years after the MRI and compared between clusters using unadjusted and adjusted regression analyses. Mean age of participants was 75.0. With 16 years of follow-up, mean YoL was 11.3; YHL, 8.0; and YAL, 8.4. Outcomes differed significantly between clusters. With or without adjustments, outcomes were all significantly better in the normal than complex infarct cluster. The three remaining clusters had intermediate results, significantly different from the normal and complex infarct clusters but not usually from one another. Over 16 years of follow-up, participants in the complex infarct cluster (n = 368) spent the largest percentage of their 8.4 years alive being sick (38%) and not able (38%). Findings on MRI scans in elderly adults are associated not only with long-term survival, but also with long-term self-rated health and limitation in ADLs. The combination of infarcts and leukoaraiosis carried the worst prognosis, presumably reflecting small vessel disease.

Trends in life expectancy and healthy life years at birth and age 65 in the UK, 2008–2016, and other countries of the EU28: An observational cross-sectional study

BackgroundPopulation ageing is associated with rising healthcare expenditure. To inform policy and adapt health systems accordingly, a detailed quantitative analysis of the different components of ageing and other factors that influence cost dynamics is needed.MethodsWe use dynamic microsimulation to project healthcare expenditure in Austria and disentangle the effects of changes in longevity, population age-structure, healthy life years and socio-economic health disparities. By combining price weights for healthcare services with information on healthcare consumption from the Austrian Health Interview Survey, we construct average cost profiles by gender, age, and education. These profiles, aligned with the System of Health Accounts, are integrated into the microDEMS model, along with official population projections, to estimate expenditure scenarios until 2060. We examine the relationship between rising life expectancy and changes in healthy life years and assess the potential impact of closing the gap in costs currently observed between education groups. Total and per-capita cost trajectories are derived and evaluated against two indicators for the size of the labor force to assess economic implications.ResultsIn all scenarios, demographic ageing increases the financial burden on the economically active population, even with morbidity compression. Nearly two-thirds of the projected cost increase stems from declining mortality, while one-third results from age-structure changes. Per-capita costs rise by 26% under a morbidity expansion scenario but could decrease by 5% if lower mortality is accompanied by an extension of healthy life years and a reduction in socio-economic health disparities. In economic terms, costs per working-age person increase by 12% to 48%, depending on the scenario. When adjusting for labor force expansion and the associated economic benefits, the increase ranges between 5% and 39%.ConclusionsRising healthcare expenditure poses a major challenge in an ageing society. However, policies that extend healthy life years and reduce socio-economic disparities offer viable strategies to significantly mitigate the economic impact of ageing.

Background While life expectancy (LE) is rising, years lived with severe health impairments especially due to chronic diseases also increase. Diabetes is one of the major chronic diseases with high potential for co-morbidity and premature deaths needing life-long care. Therefore, information about loss in LE and healthy life years (HLY) in people with diabetes compared to people without diabetes is essential for assessing the burden of diabetes. Methods Data on all-cause mortality rates for the general population of Germany in 2014 was drawn from the Federal Statistical Office; mortality rate ratios for people with diabetes compared to people without diabetes were based on claims data from about 70 million people covered by statutory health insurances in 2014. Data of three nationwide health telephone surveys conducted among adults in Germany 2009-2012 (n ∼ 60,000) were used to assess severe health impairments defined as self-reported limitations in daily activities due to diseases for at least six months in people with and without diabetes. Based on these figures, estimates on LE and HLY could be calculated by sex and 5-year age-groups for people with and without diabetes aged ≥ 30 years. Results In both sexes and in all 5-year age-groups, LE and HLY were substantially lower for people with than for people without diabetes. For example, among women in the age group 30-34 years, LE and HLY estimates were 48.0 and 36.4 years for those with diabetes compared to 54.9 and 47.6 years for those without diabetes; in men, these figures were estimated as 42.6 and 32.4 years for those with diabetes compared to 50.3 and 44.1 years for those without diabetes. Differences in LE and HLY between people with and without diabetes attenuated with increasing age. Conclusions The present study revealed substantial reductions in LE and HLY related to diabetes and underlines the importance of integrating both figures in a national diabetes surveillance. Key messages There are substantial differences in life expectancy and healthy life years between people with and without diabetes. Specific intervention and prevention activities should be implemented to tackle disability in persons with diabetes.

Population aging and declining birth rates are key demographic trends of the 21st century. While the overall life expectancy and healthy life years increase, the quality of life and functional capacity worsens due to non-communicable diseases strongly related to aging. Therefore, aging citizens are often vulnerable to food insecurity. The aim of this paper is to provide insights into the physical accessibility of fresh food and possible alternatives within the setting of an aging society in Antwerp (Belgium), a metropolitan city at the heart of the EU Reference Site ‘Three Rivers Food Delta’. We demonstrate that a large number of the Antwerp suburban areas in which 15 to 25% of current inhabitants are already over 65 years old are confronted with problematic physical accessibility of food due to long walking distances to the nearest food shop. E-commerce has the potential to provide better access to fresh food. This is especially relevant for people with specific needs, such as health-related diets, dysphagia, and/or limited mobility. However, e-commerce introduces new inequalities, as those who would benefit the most from digital accessibility currently use it least. Hence, the organization of fresh food access requires a more thoughtful organization of the ‘last mile’ and possible alternatives to home delivery. This makes food accessibility an urgent factor of concern in public health and healthy aging in the Antwerp suburban areas.

BackgroundOverweight older adults are often counseled to lose weight, even though there is little evidence of excess mortality in that age group. Overweight and underweight may be more associated with health status than with mortality, but few clinical trials of any kind have been based on maximizing years of healthy life (YHL), as opposed to years of life (YOL).ObjectiveThis paper examines the relationship of body mass index (BMI) to both YHL and YOL. Results were used to determine whether clinical trials of weight-modification based on improving YHL would be more powerful than studies based on survival.DesignWe used data from a cohort of 4,878 non-smoking men and women aged 65–100 at baseline (mean age 73) and followed 7 years. We estimated mean YHL and YOL in four categories of BMI: underweight, normal, overweight, and obese.ResultsSubjects averaged 6.3 YOL and 4.6 YHL of a possible 7 years. Both measures were higher for women and whites. For men, none of the BMI groups was significantly different from the normal group on either YOL or YHL. For women, the obese had significantly lower YHL (but not YOL) than the normals, and the underweight had significantly lower YOL and YHL. The overweight group was not significantly different from the normal group on either measure.ConclusionsClinical trials of weight loss interventions for obese older women would require fewer participants if YHL rather than YOL was the outcome measure. Interventions for obese men or for the merely overweight are not likely to achieve differences in either YOL or YHL. Evaluations of interventions for the underweight (which would presumably address the causes of their low weight) may be conducted efficiently using either outcome measure.

BackgroundLife expectancy has been increasing during the last century within the European Union (EU). To measure progress in population health it is no longer sufficient to focus on the duration of life but quality of life should be considered. Healthy Life Years (HLY) allow estimating the quality of the remaining years that a person is expected to live, in terms of being free of long-standing activity limitation. The Joint Action on Healthy Life Years (JA: EHLEIS) is a joint action of European Member States (MS) and the European Union aiming at analysing trends, patterns and differences in HLY, as well as in other Summary Measures of Population Health (SMPH) indicators, across the European member states.MethodsThe JA: EHLEIS consolidates existing information on life and health expectancy by maximising the European comparability; by analysing trends in HLY within the EU; by analysing the evolution of the differences in HLY between Member States; and by identifying both macro-level as micro-level determinants of the inequalities in HLY. The JA: EHLEIS works in collaboration with the USA, Japan and OECD on the development of new SMPHs to be used globally. To strengthen the utility of the HLY for policy-making, annual meetings with policy-makers are planned.ResultsThe information system allows the estimation of a set of health indicators (morbidity and disability prevalence, life and health expectancies) for Europe, Member States and shortly their regional levels. An annual country report on HLY in the national languages is available. The JA: EHLEIS is developing statistical attribution and decomposition tools which will be helpful to determine the impact of specific diseases, life styles or other determinants on differences in HLY. Through a set of international workshops the JA: EHLEIS aims to develop a blueprint for an international harmonized Summary Measure of Population Health.ConclusionThe JA: EHLEIS objectives are to monitor progress towards the headline target of the Europe 2020 strategy of increasing HLY by 2 years by 2020 and to support policy development by identifying the main determinants of active and healthy ageing in Europe.

BackgroundIn some countries, including Japan—the leading country in terms of longevity, life expectancy has been increasing; meanwhile, healthy life years have not kept pace, necessitating an effective health policy to narrow the gap.ObjectiveThe aim of this study is to develop a prediction model for healthy life years without activity limitations and deploy the model in a health policy to prolong healthy life years.MethodsThe Comprehensive Survey of Living Conditions, a cross-sectional national survey of Japan, was conducted by the Japanese Ministry of Health, Labour and Welfare in 2013, 2016, and 2019. The data from 1,537,773 responders were used for modelling using machine learning. All participants were randomly split into training (n=1,383,995, 90%,) and test (n=153,778, 10%) subsets. Extreme gradient boosting classifier was implemented. Activity limitations were set as the target. Age, sex, and 40 types of diseases or injuries were included as features. Healthy life years without activity limitations were calculated by incorporating the predicted prevalence rate of activity limitations in a life table. For the wide utility of the model in individuals, we developed an application tool for the model.ResultsIn the groups without (n=1,329,901) and with (n=207,872) activity limitations, the median age was 47 (IQR 30-64) and 69 (IQR 54-80) years, respectively (P<.001); female sex comprised 51.3% (n=681,794) in the group without activity limitations and 56.9% (n=118,339) in the group with activity limitations (P<.001). A total of 42 features were included in the feature set. Age had the highest impact on model accuracy, followed by depression or other mental diseases; back pain; bone fracture; other neurological disorders, pain, or paralysis; stroke, cerebral hemorrhage, or infarction; arthritis; Parkinson disease; dementia; and other injuries or burns. The model exhibited high performance with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.842-0.849) with exact calibration for the average probability and fraction of positives. The prediction results were consistent with the observed values of healthy life years for both sexes in each year (range of difference between predictive and observed values: −0.89 to 0.16 in male and 0.61 to 1.23 in female respondents). We applied the prediction model to a regional health policy to prolong healthy life years by adjusting the representative predictors to a target prevalence rate. Additionally, we presented the health condition without activity limitations index, followed by the application development for individual health promotion.ConclusionsThe prediction model will enable national or regional governments to establish an effective health promotion policy for risk prevention at the population and individual levels to prolong healthy life years. Further investigation is needed to validate the model’s adaptability to various ethnicities and, in particular, to countries where the population exhibits a short life span.

The European Union has used Healthy Life Years (HLY) as an indicator to monitor the health of its aging populations. Scholarly and popular interest in HLY across countries has grown, particularly regarding the ranking of countries. It is important to note that HLY is based on self-assessments of activity limitations, raising the possibility that it might be influenced by differences in health reporting behaviours between populations, a phenomenon known as differential item functioning (DIF). We estimated DIF-adjusted HLY at age 50 for Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, and Sweden to determine the extent to which differences in HLY might be influenced by reporting heterogeneity across countries. We used anchoring vignettes, taken from the 2004 Survey of Health, Ageing and Retirement in Europe, to estimate DIF-adjusted prevalence rates of activity limitations measured by the Global Activity Limitations Indicator (GALI). The Sullivan method was used to calculate DIF-adjusted HLY. Changes in HLY before and after adjustment ranged from a 1.20-year decrease for men in Italy to a 1.61-year increase for women in Spain. Adjustment for DIF produced changes in the rankings of the countries by HLY, with upward and downward movements of up to three positions. Our results show that DIF is likely to affect HLY estimates, thereby posing a challenge to the validity of comparisons of HLY across European countries. The findings suggest that HLY should be used to monitor population health status within a country, rather than to make comparisons across countries.